Probabilistic modeling of electric vehicle charging load for probabilistic load flow

Abstract

With an increasing concern about environmental pollution and rising price of fossil fuels, electric vehicles (EVs) are becoming an important alternative energy source in a transportation sector. A rapid deployment of EVs may have significant impacts on demand for electricity in a power system. Also, a large-scale deployment of EVs can introduce greater uncertainty in EV charging patterns and loads. This paper presents a Stratified Latin Hypercube Sampling (SLHS)-based probabilistic load flow (PLF) method incorporating electric vehicle charging load. In this paper, probabilistic EV charging load is modeled by using the EV penetration level, hourly traffic patterns, and EV charging scenarios. The Monte Carlo Simulation (MCS)-based PLF requires a significant amount of computation time to obtain accurate results. In this paper, SLHS technique is also applied to reduce the computation time of the PLF. A numerical example is presented to show the performance of the proposed method.